Model and analyze financial and economic systems using statistical methods
Econometrics Toolbox™ provides functions for modeling economic data. You can select and estimate economic models for simulation and forecasting. For time series modeling and analysis, the toolbox includes univariate Bayesian linear regression, univariate ARIMAX/GARCH composite models with several GARCH variants, multivariate VARX models, and cointegration analysis. It also provides methods for modeling economic systems using state-space models and for estimating using the Kalman filter. You can use a variety of diagnostics for model selection, including hypothesis tests, unit root, stationarity, and structural change.
Time Series Modeling
- Perform modeling tasks, including data preprocessing, data visualization, model identification, and parameter estimations.
- Compare econometric models to ensure the best fit to the data.
- Share results and generate MATLAB code for repeat use.
Supported models include AR, MA, ARMA, ARIMA, SARIMA, and ARIMAX.
Markov Chain Models
- Create and simulate discrete-time Markov chains.
- Determine Markov chain asymptotic behavior.
- Compute state redistributions, hitting probabilities, and expected hitting times.
- Create and simulate time-invariant or time-varying state-space models.
- Estimate model parameters from full data sets or from data sets with missing data using the Kalman filter.
Markov Switching Models
- Analyze multivariate time series data with structural breaks and unobserved latent states.
Create a Markov-switching model for analyzing multivariate time series data with structural breaks and unobserved latent states
Discrete-Time Markov Chains
Compute hitting probabilities and expected hitting times
Computational Finance Suite
The MATLAB Computational Finance Suite is a set of 12 essential products that enables you to develop quantitative applications for risk management, investment management, econometrics, pricing and valuation, insurance, and algorithmic trading.